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» A Model for Structured Document Retrieval: Empirical Investi...
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WWW
2011
ACM
13 years 3 months ago
Learning to rank with multiple objective functions
We investigate the problem of learning to rank for document retrieval from the perspective of learning with multiple objective functions. We present solutions to two open problems...
Krysta Marie Svore, Maksims Volkovs, Christopher J...
ICDCS
2005
IEEE
14 years 2 months ago
Using a Layered Markov Model for Distributed Web Ranking Computation
The link structure of the Web graph is used in algorithms such as Kleinberg’s HITS and Google’s PageRank to assign authoritative weights to Web pages and thus rank them. Both ...
Jie Wu, Karl Aberer
ICDAR
2009
IEEE
13 years 6 months ago
Lexicon-Based Word Recognition Using Support Vector Machine and Hidden Markov Model
Hybrid of Neural Network (NN) and Hidden Markov Model (HMM) has been popular in word recognition, taking advantage of NN discriminative property and HMM representational capabilit...
Abdul Rahim Ahmad, Christian Viard-Gaudin, Marzuki...
NIPS
2008
13 years 10 months ago
Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text
In this paper, we address the question of what kind of knowledge is generally transferable from unlabeled text. We suggest and analyze the semantic correlation of words as a gener...
Yi Zhang 0010, Jeff Schneider, Artur Dubrawski
GFKL
2006
Springer
78views Data Mining» more  GFKL 2006»
14 years 10 days ago
Putting Successor Variety Stemming to Work
Stemming algorithms find canonical forms for inflected words, e. g. for declined nouns or conjugated verbs. Since such a unification of words with respect to gender, number, time, ...
Benno Stein, Martin Potthast